Collaborative filtering is a very useful general technique for exploiting the
preference patterns of a group of users to predict the utility of items to a
particular user. Previous research has studied several probabilistic graphic
models for collaborative filtering with promising results. However, while these
models have succeeded in capturing the similarity among users and items in one
way or the other, none of them has considered the fact that users with similar
interests in items can have very different rating patterns; some users tend to
assign a higher rating to all items than other users. In this paper, we propose
and study of two new graphic models that address the distinction between user
preferences and ratings. In one model, called the decoupled model, we introduce
two different variables to decouple a users preferences FROM his ratings. IN
the other, called the preference model, we model the orderings OF items
preferred BY a USER, rather than the USERs numerical ratings of items.
Empirical study over two datasets of movie ratings shows that appropriate
modeling of the distinction between user preferences and ratings improves the
performance substantially and consistently. Specifically, the proposed
decoupled model outperforms all five existing approaches that we compare with
significantly, but the preference model is not very successful. These results
suggest that explicit modeling of the underlying user preferences is very
important for collaborative filtering, but we can not afford ignoring the
rating information completely.

本文研究和提出了两种新的图形模型，解决了用户偏好和评分之间的区别问题，通过两个电影评分数据集的实证研究，证明了明确建模用户偏好对于协同过滤非常重要，但是不能完全忽略评分信息。